Routines Overview

Routines are deterministic Python workflows that process data consistently. Unlike skills which load instructions into context for the LLM to interpret, routines are lightweight Python files that execute directly and return results—no tokens spent on interpretation.

Why Routines Matter

When testing agents, we found they waste enormous amounts of tokens processing data and computing indicators during runtime. An agent might get candles, then write Python code to compute EMAs and support/resistance—spending tokens on computation that should be deterministic. By moving this into routines:

Session time dropped from 2 minutes to under 1 minute
Token usage reduced significantly
Results became reproducible and debuggable

If Python code can be created by the agent, it should be encapsulated in a routine so it can be reused without spending tokens.

Routines vs Skills

Skills and routines serve different purposes:

Aspect	Skills	Routines
Format	Markdown instructions + scripts	Python file with config class
Execution	LLM interprets and follows instructions	Python code runs directly
Loading	Loaded into system prompt when detected	Never loaded into memory—just executed
Use case	Teaching agent general capabilities	Specific, repeatable tasks
Token cost	Tokens spent interpreting instructions	Zero tokens for execution
Output	Variable (LLM interpretation)	Deterministic (same input → same output)

When to use routines: Specific tasks you want done reliably every time—fetching market data, running technical analysis, generating reports, monitoring conditions. When to use skills: Teaching an agent how to approach a category of problems where interpretation and judgment are needed.

Routines vs LLM Reasoning

Aspect	LLM Reasoning	Routines
Execution	Probabilistic	Deterministic
Purpose	Strategy decisions	Data processing and automation
Variability	May produce different outputs	Same input → same output
Cost	LLM tokens	None
Speed	Seconds	Milliseconds

Routine Structure

Every routine is a Python file with two components:

Config class - A Pydantic BaseModel defining the arguments/parameters
Async run method - The entry point that executes the routine

# routines/hello_world.py
from pydantic import BaseModel, Field
from telegram.ext import ContextTypes


class Config(BaseModel):
    """Simple hello world example routine."""

    name: str = Field(default="World", description="Name to greet")
    repeat: int = Field(default=1, description="Number of times to repeat")
    uppercase: bool = Field(default=False, description="Use uppercase")


async def run(config: Config, context: ContextTypes.DEFAULT_TYPE) -> str:
    """Execute the routine."""
    greeting = f"Hello, {config.name}!"

    if config.uppercase:
        greeting = greeting.upper()

    return "\n".join([greeting] * config.repeat)

The Config class docstring becomes the routine’s description in the UI. Use Field(description=...) to document each parameter.

Execution Modes

One-Shot Routines

Execute once and return a result. Can be scheduled to run at regular intervals.

# routines/top_movers.py
from pydantic import BaseModel, Field
from telegram.ext import ContextTypes


class Config(BaseModel):
    """Fetch top movers from exchange."""

    top_n: int = Field(default=10, description="Number of top movers")
    include_volume: bool = Field(default=True, description="Include volume data")
    include_losers: bool = Field(default=True, description="Include top losers")


async def run(config: Config, context: ContextTypes.DEFAULT_TYPE) -> str:
    # Fetch and process exchange data
    async with aiohttp.ClientSession() as session:
        # ... fetch top movers
        pass
    return f"Top {config.top_n} gainers: ..."

Continuous Routines

Run indefinitely with a while loop until stopped. Useful for monitoring and alerting. Mark with CONTINUOUS = True.

# routines/price_monitor.py
from pydantic import BaseModel, Field
from telegram.ext import ContextTypes

# Mark as continuous routine - has internal loop
CONTINUOUS = True


class Config(BaseModel):
    """Live price monitor with configurable alerts."""

    connector: str = Field(default="binance", description="CEX connector name")
    trading_pair: str = Field(default="BTC-USDT", description="Trading pair to monitor")
    threshold_pct: float = Field(default=1.0, description="Alert threshold in %")
    interval_sec: int = Field(default=10, description="Check interval in seconds")


async def run(config: Config, context: ContextTypes.DEFAULT_TYPE) -> str:
    """Monitor price continuously until cancelled."""
    chat_id = context._chat_id

    while True:
        try:
            # Get current price and check threshold
            # Send alert via context.bot.send_message() if triggered
            await asyncio.sleep(config.interval_sec)
        except asyncio.CancelledError:
            return "Monitor stopped"

Continuous routines must handle asyncio.CancelledError to clean up gracefully when stopped.

Running Routines

From Telegram

Access routines through the Condor menu:

/routines → Select routine → Configure → Run

Options when running:

Run once - Execute immediately in the current chat
Run in background - Execute and send output to chat when complete
Schedule - Run at regular intervals (e.g., every 30 seconds, hourly)

To stop a running or scheduled routine:

/routines → Running → Select routine → Stop

From Agent Sessions

Agents can invoke routines as part of their decision process—see Calling Routines below.

Types of Routines

Global Routines

Routines in ~/condor/routines/ are available to all agents. These include built-in routines for common tasks like technical analysis, funding rates, and volume analysis.

Agent-Specific Routines

Routines in an agent’s routines/ folder are specific to that agent:

trading_agents/my_scalper/
└── routines/
    ├── support_resistance_ema_levels.py
    └── process_news.py

This design lets you share an agent folder with someone else—they get everything needed to run it.

Agent-specific routines override global ones with the same name.

Calling Routines

Agents invoke routines via MCP tools:

# From within an agent's decision process
vwap = await mcp_tools.run_routine(
    routine="indicators.vwap",
    params={
        "connector": "binance",
        "trading_pair": "BTC-USDT",
        "periods": 50
    }
)

if current_price < vwap["vwap"] * 0.98:
    # Price 2% below VWAP - consider buying
    pass

View Available Routines

Ask Condor what routines are available:

You: What routines are available?

Condor: Available routines for SOL Scalper:

Agent-specific:
- momentum_scanner: Scan for momentum breakouts

Global:
- technical_analysis: EMA, RSI, support/resistance
- funding_rates: Fetch perpetual funding rates
- volume_analysis: Volume profile and VWAP

Built-in Routines

Condor includes several built-in routines in ~/condor/routines/:

Routine	Type	Description
`hello_world`	One-shot	Simple example routine
`market_scanner`	One-shot	Scan markets for trading opportunities
`bot_report`	One-shot	Generate performance reports for running bots
`arb_check`	One-shot	Check for arbitrage opportunities
`price_monitor`	Continuous	Monitor price and alert on threshold changes

Creating Routines with Condor

Condor can write routines for you. Simply describe what you want:

You: Create a routine that monitors funding rates on Binance perpetuals
     and alerts me when any rate exceeds 0.01%

Condor generates the Python file, and you can immediately run or schedule it. This is particularly powerful because:

Zero coding required - Describe what you want in natural language
Instant feedback - Run the routine and see results immediately
Iterative refinement - Ask Condor to modify the routine based on output

You can create routines from your phone via Telegram. Describe what you need, have Condor write the code, schedule it, and receive reports—all without touching a keyboard.

Condor

Getting Started

Trading Agents

Executors

Routines

Bots

Other Condor Features

Routines Overview

Why Routines Matter

Routines vs Skills

Routines vs LLM Reasoning

Routine Structure

Execution Modes

One-Shot Routines

Continuous Routines

Running Routines

From Telegram

From Agent Sessions

Types of Routines

Global Routines

Agent-Specific Routines

Calling Routines

View Available Routines

Built-in Routines

Creating Routines with Condor

​Why Routines Matter

​Routines vs Skills

​Routines vs LLM Reasoning

​Routine Structure

​Execution Modes

​One-Shot Routines

​Continuous Routines

​Running Routines

​From Telegram

​From Agent Sessions

​Types of Routines

​Global Routines

​Agent-Specific Routines

​Calling Routines

​View Available Routines

​Built-in Routines

​Creating Routines with Condor

Why Routines Matter

Routines vs Skills

Routines vs LLM Reasoning

Routine Structure

Execution Modes

One-Shot Routines

Continuous Routines

Running Routines

From Telegram

From Agent Sessions

Types of Routines

Global Routines

Agent-Specific Routines

Calling Routines

View Available Routines

Built-in Routines

Creating Routines with Condor